The Push Forward in Rehabilitation
Validation of a Machine Learning Method for Detection of Wheelchair Propulsion Type
RMA van der Slikke (TU Delft - Biomechatronics & Human-Machine Control, The Hague University of Applied Sciences)
Arie-Willem de Leeuw (The Hague University of Applied Sciences)
Aleid de Rooij (Leiden University Medical Center, Basalt Revalidatie)
M.A.M. Berger (The Hague University of Applied Sciences, Basalt Revalidatie)
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Abstract
Within rehabilitation, there is a great need for a simple method to monitor wheelchair use, especially whether it is active or passive. For this purpose, an existing measurement technique was extended with a method for detecting self- or attendant-pushed wheelchair propulsion. The aim of this study was to validate this new detection method by comparison with manual annotation of wheelchair use. Twenty-four amputation and stroke patients completed a semi-structured course of active and passive wheelchair use. Based on a machine learning approach, a method was developed that detected the type of movement. The machine learning method was trained based on the data of a single-wheel sensor as well as a setup using an additional sensor on the frame. The method showed high accuracy (F1 = 0.886, frame and wheel sensor) even if only a single wheel sensor was used (F1 = 0.827). The developed and validated measurement method is ideally suited to easily determine wheelchair use and the corresponding activity level of patients in rehabilitation.
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